Overview

Dataset statistics

Number of variables20
Number of observations13096
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 MiB
Average record size in memory168.0 B

Variable types

Numeric19
Categorical1

Alerts

(Bleed Enthalpy) - s17 is highly overall correlated with (HPC Outlet Static Pressure) (psia) - s11 and 1 other fieldsHigh correlation
(Bypass Ratio) - s15 is highly overall correlated with (Corrected Fan Speed) (rpm) - s13 and 5 other fieldsHigh correlation
(Corrected Core Speed) (rpm) - s14 is highly overall correlated with (Physical Core Speed) (rpm) - s9High correlation
(Corrected Fan Speed) (rpm) - s13 is highly overall correlated with (Bypass Ratio) - s15 and 8 other fieldsHigh correlation
(HPC Outlet Pressure) (psia) - s7 is highly overall correlated with (Bypass Ratio) - s15 and 8 other fieldsHigh correlation
(HPC Outlet Static Pressure) (psia) - s11 is highly overall correlated with (Bleed Enthalpy) - s17 and 9 other fieldsHigh correlation
(High-Pressure Turbines Cool Air Flow) - s20 is highly overall correlated with (Corrected Fan Speed) (rpm) - s13 and 5 other fieldsHigh correlation
(LPC Outlet Temperature) (â—¦R) - s2 is highly overall correlated with (Corrected Fan Speed) (rpm) - s13 and 5 other fieldsHigh correlation
(LPT Outlet Temperature) (â—¦R) - s4 is highly overall correlated with (Bypass Ratio) - s15 and 8 other fieldsHigh correlation
(Low-Pressure Turbines Cool Air Flow) - s21 is highly overall correlated with (Corrected Fan Speed) (rpm) - s13 and 5 other fieldsHigh correlation
(Physical Core Speed) (rpm) - s9 is highly overall correlated with (Corrected Core Speed) (rpm) - s14High correlation
(Physical Fan Speed) (rpm) - s8 is highly overall correlated with (Bypass Ratio) - s15 and 8 other fieldsHigh correlation
(Ratio of Fuel Flow to Ps30) (pps/psia) - s12 is highly overall correlated with (Bleed Enthalpy) - s17 and 9 other fieldsHigh correlation
Cycle is highly overall correlated with RULHigh correlation
RUL is highly overall correlated with CycleHigh correlation
(Bypass-Duct Pressure) (psia) - s6 is highly imbalanced (80.6%)Imbalance
Setting 1 - c1 has 227 (1.7%) zerosZeros
Setting 2 - c2 has 1272 (9.7%) zerosZeros

Reproduction

Analysis started2024-05-24 14:57:40.948184
Analysis finished2024-05-24 14:58:23.168418
Duration42.22 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Engine
Real number (ℝ)

Distinct100
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.543907
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.6 KiB
2024-05-24T18:28:23.255376image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q128
median52
Q376
95-th percentile95
Maximum100
Range99
Interquartile range (IQR)48

Descriptive statistics

Standard deviation28.289423
Coefficient of variation (CV)0.54884127
Kurtosis-1.1713121
Mean51.543907
Median Absolute Deviation (MAD)24
Skewness0.0017422866
Sum675019
Variance800.29147
MonotonicityIncreasing
2024-05-24T18:28:23.382040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49 303
 
2.3%
93 244
 
1.9%
91 234
 
1.8%
62 232
 
1.8%
12 217
 
1.7%
81 213
 
1.6%
76 205
 
1.6%
34 203
 
1.6%
100 198
 
1.5%
35 198
 
1.5%
Other values (90) 10849
82.8%
ValueCountFrequency (%)
1 31
 
0.2%
2 49
 
0.4%
3 126
1.0%
4 106
0.8%
5 98
0.7%
6 105
0.8%
7 160
1.2%
8 166
1.3%
9 55
 
0.4%
10 192
1.5%
ValueCountFrequency (%)
100 198
1.5%
99 97
 
0.7%
98 121
0.9%
97 134
1.0%
96 97
 
0.7%
95 89
 
0.7%
94 133
1.0%
93 244
1.9%
92 150
1.1%
91 234
1.8%

Cycle
Real number (ℝ)

HIGH CORRELATION 

Distinct303
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.836515
Minimum1
Maximum303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.6 KiB
2024-05-24T18:28:23.510095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q133
median69
Q3113
95-th percentile173
Maximum303
Range302
Interquartile range (IQR)80

Descriptive statistics

Standard deviation53.057749
Coefficient of variation (CV)0.6905278
Kurtosis0.20570471
Mean76.836515
Median Absolute Deviation (MAD)39
Skewness0.72433178
Sum1006251
Variance2815.1248
MonotonicityNot monotonic
2024-05-24T18:28:23.652305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 100
 
0.8%
17 100
 
0.8%
31 100
 
0.8%
30 100
 
0.8%
29 100
 
0.8%
28 100
 
0.8%
27 100
 
0.8%
26 100
 
0.8%
25 100
 
0.8%
24 100
 
0.8%
Other values (293) 12096
92.4%
ValueCountFrequency (%)
1 100
0.8%
2 100
0.8%
3 100
0.8%
4 100
0.8%
5 100
0.8%
6 100
0.8%
7 100
0.8%
8 100
0.8%
9 100
0.8%
10 100
0.8%
ValueCountFrequency (%)
303 1
< 0.1%
302 1
< 0.1%
301 1
< 0.1%
300 1
< 0.1%
299 1
< 0.1%
298 1
< 0.1%
297 1
< 0.1%
296 1
< 0.1%
295 1
< 0.1%
294 1
< 0.1%

Setting 1 - c1
Real number (ℝ)

ZEROS 

Distinct150
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.1178986 × 10-5
Minimum-0.0082
Maximum0.0078
Zeros227
Zeros (%)1.7%
Negative6470
Negative (%)49.4%
Memory size204.6 KiB
2024-05-24T18:28:23.827651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.0082
5-th percentile-0.0036
Q1-0.0015
median-0
Q30.0015
95-th percentile0.0036
Maximum0.0078
Range0.016
Interquartile range (IQR)0.003

Descriptive statistics

Standard deviation0.0022026851
Coefficient of variation (CV)-197.038
Kurtosis0.0088020963
Mean-1.1178986 × 10-5
Median Absolute Deviation (MAD)0.0015
Skewness-0.0022824912
Sum-0.1464
Variance4.8518216 × 10-6
MonotonicityNot monotonic
2024-05-24T18:28:23.959471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0003 249
 
1.9%
-0.0005 245
 
1.9%
-0.0006 244
 
1.9%
0.0001 244
 
1.9%
0.0004 239
 
1.8%
-0.001 237
 
1.8%
0.0005 235
 
1.8%
0.0006 234
 
1.8%
-0.0002 234
 
1.8%
0.0002 233
 
1.8%
Other values (140) 10702
81.7%
ValueCountFrequency (%)
-0.0082 1
 
< 0.1%
-0.0079 1
 
< 0.1%
-0.0077 3
< 0.1%
-0.0074 1
 
< 0.1%
-0.0073 2
< 0.1%
-0.0071 4
< 0.1%
-0.007 1
 
< 0.1%
-0.0069 3
< 0.1%
-0.0068 2
< 0.1%
-0.0067 2
< 0.1%
ValueCountFrequency (%)
0.0078 1
 
< 0.1%
0.0077 1
 
< 0.1%
0.0076 4
< 0.1%
0.0075 1
 
< 0.1%
0.0072 2
 
< 0.1%
0.007 1
 
< 0.1%
0.0069 1
 
< 0.1%
0.0068 2
 
< 0.1%
0.0066 2
 
< 0.1%
0.0064 5
< 0.1%

Setting 2 - c2
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2379352 × 10-6
Minimum-0.0006
Maximum0.0007
Zeros1272
Zeros (%)9.7%
Negative5869
Negative (%)44.8%
Memory size204.6 KiB
2024-05-24T18:28:24.070690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.0006
5-th percentile-0.0004
Q1-0.0002
median-0
Q30.0003
95-th percentile0.0005
Maximum0.0007
Range0.0013
Interquartile range (IQR)0.0005

Descriptive statistics

Standard deviation0.00029403057
Coefficient of variation (CV)69.380618
Kurtosis-1.1315659
Mean4.2379352 × 10-6
Median Absolute Deviation (MAD)0.0003
Skewness0.016358468
Sum0.0555
Variance8.6453974 × 10-8
MonotonicityNot monotonic
2024-05-24T18:28:24.176356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
-0.0002 1359
10.4%
-0.0003 1354
10.3%
0.0003 1321
10.1%
0.0001 1320
10.1%
0.0004 1290
9.9%
0.0002 1280
9.8%
-0.0001 1273
9.7%
0 1272
9.7%
-0.0004 1245
9.5%
0.0005 681
5.2%
Other values (4) 701
5.4%
ValueCountFrequency (%)
-0.0006 13
 
0.1%
-0.0005 625
4.8%
-0.0004 1245
9.5%
-0.0003 1354
10.3%
-0.0002 1359
10.4%
-0.0001 1273
9.7%
0 1272
9.7%
0.0001 1320
10.1%
0.0002 1280
9.8%
0.0003 1321
10.1%
ValueCountFrequency (%)
0.0007 5
 
< 0.1%
0.0006 58
 
0.4%
0.0005 681
5.2%
0.0004 1290
9.9%
0.0003 1321
10.1%
0.0002 1280
9.8%
0.0001 1320
10.1%
0 1272
9.7%
-0.0001 1273
9.7%
-0.0002 1359
10.4%

(LPC Outlet Temperature) (â—¦R) - s2
Real number (ℝ)

HIGH CORRELATION 

Distinct262
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean642.47509
Minimum641.13
Maximum644.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.6 KiB
2024-05-24T18:28:24.307391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum641.13
5-th percentile641.84
Q1642.1975
median642.46
Q3642.74
95-th percentile643.16
Maximum644.3
Range3.17
Interquartile range (IQR)0.5425

Descriptive statistics

Standard deviation0.40089934
Coefficient of variation (CV)0.00062399204
Kurtosis0.078362872
Mean642.47509
Median Absolute Deviation (MAD)0.27
Skewness0.22496247
Sum8413853.8
Variance0.16072028
MonotonicityNot monotonic
2024-05-24T18:28:24.448278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
642.49 179
 
1.4%
642.45 151
 
1.2%
642.54 149
 
1.1%
642.5 148
 
1.1%
642.34 142
 
1.1%
642.36 142
 
1.1%
642.43 136
 
1.0%
642.39 136
 
1.0%
642.67 135
 
1.0%
642.38 135
 
1.0%
Other values (252) 11643
88.9%
ValueCountFrequency (%)
641.13 1
 
< 0.1%
641.15 1
 
< 0.1%
641.26 1
 
< 0.1%
641.29 1
 
< 0.1%
641.3 2
< 0.1%
641.32 2
< 0.1%
641.34 4
< 0.1%
641.35 1
 
< 0.1%
641.37 1
 
< 0.1%
641.39 1
 
< 0.1%
ValueCountFrequency (%)
644.3 1
< 0.1%
644.07 1
< 0.1%
644.05 1
< 0.1%
644.04 1
< 0.1%
644.03 2
< 0.1%
643.93 1
< 0.1%
643.91 1
< 0.1%
643.89 1
< 0.1%
643.88 1
< 0.1%
643.86 2
< 0.1%
Distinct2361
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1588.0992
Minimum1569.04
Maximum1607.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.6 KiB
2024-05-24T18:28:24.579008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1569.04
5-th percentile1580.1
Q11584.6
median1587.99
Q31591.3625
95-th percentile1596.55
Maximum1607.55
Range38.51
Interquartile range (IQR)6.7625

Descriptive statistics

Standard deviation5.0032739
Coefficient of variation (CV)0.0031504795
Kurtosis0.060664027
Mean1588.0992
Median Absolute Deviation (MAD)3.385
Skewness0.15805855
Sum20797747
Variance25.03275
MonotonicityNot monotonic
2024-05-24T18:28:24.708295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1588.4 20
 
0.2%
1588.59 19
 
0.1%
1587.08 19
 
0.1%
1591.25 19
 
0.1%
1589.54 19
 
0.1%
1586.41 18
 
0.1%
1586.97 18
 
0.1%
1587.58 18
 
0.1%
1586.73 18
 
0.1%
1586.29 18
 
0.1%
Other values (2351) 12910
98.6%
ValueCountFrequency (%)
1569.04 1
< 0.1%
1570.12 1
< 0.1%
1571.02 1
< 0.1%
1571.13 1
< 0.1%
1572.37 1
< 0.1%
1572.84 1
< 0.1%
1572.91 1
< 0.1%
1573.06 1
< 0.1%
1573.19 1
< 0.1%
1573.23 1
< 0.1%
ValueCountFrequency (%)
1607.55 1
< 0.1%
1607.16 1
< 0.1%
1606.62 1
< 0.1%
1606.24 1
< 0.1%
1606.18 1
< 0.1%
1606.04 1
< 0.1%
1605.87 1
< 0.1%
1605.84 1
< 0.1%
1605.58 1
< 0.1%
1605.48 1
< 0.1%

(LPT Outlet Temperature) (â—¦R) - s4
Real number (ℝ)

HIGH CORRELATION 

Distinct2954
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1404.7354
Minimum1384.39
Maximum1433.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.6 KiB
2024-05-24T18:28:24.837146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1384.39
5-th percentile1394.46
Q11399.95
median1404.44
Q31409.05
95-th percentile1416.28
Maximum1433.36
Range48.97
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation6.6883093
Coefficient of variation (CV)0.0047612593
Kurtosis0.15466447
Mean1404.7354
Median Absolute Deviation (MAD)4.53
Skewness0.34210073
Sum18396414
Variance44.733481
MonotonicityNot monotonic
2024-05-24T18:28:24.961863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1402.53 17
 
0.1%
1404.09 16
 
0.1%
1404.77 16
 
0.1%
1407.6 15
 
0.1%
1403.1 15
 
0.1%
1400.89 15
 
0.1%
1403.93 15
 
0.1%
1406.96 15
 
0.1%
1400.01 14
 
0.1%
1400.95 14
 
0.1%
Other values (2944) 12944
98.8%
ValueCountFrequency (%)
1384.39 1
< 0.1%
1385.21 1
< 0.1%
1385.27 1
< 0.1%
1385.51 1
< 0.1%
1385.64 1
< 0.1%
1385.67 1
< 0.1%
1385.71 1
< 0.1%
1385.93 1
< 0.1%
1386.36 1
< 0.1%
1386.57 1
< 0.1%
ValueCountFrequency (%)
1433.36 1
< 0.1%
1432.95 1
< 0.1%
1432.29 1
< 0.1%
1430.85 1
< 0.1%
1430.64 1
< 0.1%
1429.89 1
< 0.1%
1429.85 1
< 0.1%
1429.66 1
< 0.1%
1429.31 1
< 0.1%
1429.15 1
< 0.1%

(Bypass-Duct Pressure) (psia) - s6
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size894.9 KiB
21.61
12704 
21.6
 
392

Length

Max length5
Median length5
Mean length4.9700672
Min length4

Characters and Unicode

Total characters65088
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row21.61
2nd row21.61
3rd row21.61
4th row21.61
5th row21.61

Common Values

ValueCountFrequency (%)
21.61 12704
97.0%
21.6 392
 
3.0%

Length

2024-05-24T18:28:25.076260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-24T18:28:25.161187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
21.61 12704
97.0%
21.6 392
 
3.0%

Most occurring characters

ValueCountFrequency (%)
1 25800
39.6%
2 13096
20.1%
. 13096
20.1%
6 13096
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 65088
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 25800
39.6%
2 13096
20.1%
. 13096
20.1%
6 13096
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 65088
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 25800
39.6%
2 13096
20.1%
. 13096
20.1%
6 13096
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 65088
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 25800
39.6%
2 13096
20.1%
. 13096
20.1%
6 13096
20.1%

(HPC Outlet Pressure) (psia) - s7
Real number (ℝ)

HIGH CORRELATION 

Distinct415
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean553.75752
Minimum550.88
Maximum555.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.6 KiB
2024-05-24T18:28:25.259147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum550.88
5-th percentile552.58
Q1553.31
median553.8
Q3554.24
95-th percentile554.8
Maximum555.84
Range4.96
Interquartile range (IQR)0.93

Descriptive statistics

Standard deviation0.68128611
Coefficient of variation (CV)0.0012302968
Kurtosis0.11207596
Mean553.75752
Median Absolute Deviation (MAD)0.46
Skewness-0.34570116
Sum7252008.5
Variance0.46415076
MonotonicityNot monotonic
2024-05-24T18:28:25.384313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
554.11 90
 
0.7%
553.81 88
 
0.7%
553.82 87
 
0.7%
554.02 86
 
0.7%
553.78 86
 
0.7%
553.93 83
 
0.6%
553.65 83
 
0.6%
553.79 83
 
0.6%
554.07 83
 
0.6%
553.8 82
 
0.6%
Other values (405) 12245
93.5%
ValueCountFrequency (%)
550.88 1
< 0.1%
550.91 1
< 0.1%
550.94 1
< 0.1%
551.01 1
< 0.1%
551.15 1
< 0.1%
551.19 2
< 0.1%
551.21 1
< 0.1%
551.23 1
< 0.1%
551.26 1
< 0.1%
551.34 2
< 0.1%
ValueCountFrequency (%)
555.84 1
< 0.1%
555.81 1
< 0.1%
555.8 1
< 0.1%
555.72 1
< 0.1%
555.69 1
< 0.1%
555.65 1
< 0.1%
555.64 1
< 0.1%
555.63 1
< 0.1%
555.61 1
< 0.1%
555.57 1
< 0.1%

(Physical Fan Speed) (rpm) - s8
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2388.071
Minimum2387.89
Maximum2388.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.6 KiB
2024-05-24T18:28:25.507154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2387.89
5-th percentile2387.98
Q12388.03
median2388.07
Q32388.11
95-th percentile2388.17
Maximum2388.3
Range0.41
Interquartile range (IQR)0.08

Descriptive statistics

Standard deviation0.057441784
Coefficient of variation (CV)2.4053634 × 10-5
Kurtosis-0.062196886
Mean2388.071
Median Absolute Deviation (MAD)0.04
Skewness0.30340178
Sum31274177
Variance0.0032995586
MonotonicityNot monotonic
2024-05-24T18:28:25.667143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
2388.05 883
 
6.7%
2388.07 863
 
6.6%
2388.04 852
 
6.5%
2388.06 821
 
6.3%
2388.08 813
 
6.2%
2388.09 772
 
5.9%
2388.03 763
 
5.8%
2388.02 756
 
5.8%
2388.1 728
 
5.6%
2388.11 685
 
5.2%
Other values (31) 5160
39.4%
ValueCountFrequency (%)
2387.89 1
 
< 0.1%
2387.91 8
 
0.1%
2387.92 6
 
< 0.1%
2387.93 20
 
0.2%
2387.94 33
 
0.3%
2387.95 64
 
0.5%
2387.96 110
 
0.8%
2387.97 187
1.4%
2387.98 296
2.3%
2387.99 371
2.8%
ValueCountFrequency (%)
2388.3 1
 
< 0.1%
2388.29 5
 
< 0.1%
2388.28 3
 
< 0.1%
2388.27 5
 
< 0.1%
2388.26 13
 
0.1%
2388.25 11
 
0.1%
2388.24 22
 
0.2%
2388.23 37
0.3%
2388.22 31
0.2%
2388.21 56
0.4%

(Physical Core Speed) (rpm) - s9
Real number (ℝ)

HIGH CORRELATION 

Distinct4047
Distinct (%)30.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9058.4074
Minimum9024.53
Maximum9155.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.6 KiB
2024-05-24T18:28:25.785584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum9024.53
5-th percentile9043.09
Q19051.02
median9057.32
Q39064.11
95-th percentile9076.2725
Maximum9155.03
Range130.5
Interquartile range (IQR)13.09

Descriptive statistics

Standard deviation11.436261
Coefficient of variation (CV)0.0012625023
Kurtosis7.5258067
Mean9058.4074
Median Absolute Deviation (MAD)6.56
Skewness1.6547139
Sum1.186289 × 108
Variance130.78805
MonotonicityNot monotonic
2024-05-24T18:28:25.915783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9059.45 16
 
0.1%
9062.01 14
 
0.1%
9060.38 13
 
0.1%
9052.93 13
 
0.1%
9054.49 13
 
0.1%
9056.96 12
 
0.1%
9058.17 12
 
0.1%
9055.84 12
 
0.1%
9059.46 12
 
0.1%
9055.07 12
 
0.1%
Other values (4037) 12967
99.0%
ValueCountFrequency (%)
9024.53 1
< 0.1%
9025 1
< 0.1%
9025.97 1
< 0.1%
9026.89 1
< 0.1%
9029.71 1
< 0.1%
9029.72 1
< 0.1%
9029.81 1
< 0.1%
9030.68 1
< 0.1%
9030.8 1
< 0.1%
9030.97 1
< 0.1%
ValueCountFrequency (%)
9155.03 1
< 0.1%
9148.85 1
< 0.1%
9148.56 1
< 0.1%
9146.81 1
< 0.1%
9146.03 1
< 0.1%
9145.88 1
< 0.1%
9145.77 1
< 0.1%
9142.37 1
< 0.1%
9142.18 1
< 0.1%
9141.92 1
< 0.1%

(HPC Outlet Static Pressure) (psia) - s11
Real number (ℝ)

HIGH CORRELATION 

Distinct136
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.416204
Minimum46.8
Maximum48.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.6 KiB
2024-05-24T18:28:26.037539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum46.8
5-th percentile47.12
Q147.27
median47.41
Q347.54
95-th percentile47.75
Maximum48.26
Range1.46
Interquartile range (IQR)0.27

Descriptive statistics

Standard deviation0.19591725
Coefficient of variation (CV)0.0041318627
Kurtosis0.22072902
Mean47.416204
Median Absolute Deviation (MAD)0.13
Skewness0.40444895
Sum620962.61
Variance0.038383567
MonotonicityNot monotonic
2024-05-24T18:28:26.160147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.41 296
 
2.3%
47.43 287
 
2.2%
47.32 273
 
2.1%
47.42 270
 
2.1%
47.35 269
 
2.1%
47.33 265
 
2.0%
47.36 261
 
2.0%
47.37 261
 
2.0%
47.5 255
 
1.9%
47.38 251
 
1.9%
Other values (126) 10408
79.5%
ValueCountFrequency (%)
46.8 1
 
< 0.1%
46.84 1
 
< 0.1%
46.86 2
 
< 0.1%
46.87 1
 
< 0.1%
46.89 3
 
< 0.1%
46.9 2
 
< 0.1%
46.91 3
 
< 0.1%
46.92 3
 
< 0.1%
46.93 8
0.1%
46.94 5
< 0.1%
ValueCountFrequency (%)
48.26 1
 
< 0.1%
48.23 1
 
< 0.1%
48.2 1
 
< 0.1%
48.18 2
< 0.1%
48.16 1
 
< 0.1%
48.15 2
< 0.1%
48.14 1
 
< 0.1%
48.13 3
< 0.1%
48.12 2
< 0.1%
48.11 3
< 0.1%

(Ratio of Fuel Flow to Ps30) (pps/psia) - s12
Real number (ℝ)

HIGH CORRELATION 

Distinct357
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean521.74772
Minimum519.38
Maximum523.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.6 KiB
2024-05-24T18:28:26.332558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum519.38
5-th percentile520.78
Q1521.38
median521.78
Q3522.15
95-th percentile522.59
Maximum523.76
Range4.38
Interquartile range (IQR)0.77

Descriptive statistics

Standard deviation0.55962675
Coefficient of variation (CV)0.0010726003
Kurtosis0.22539176
Mean521.74772
Median Absolute Deviation (MAD)0.38
Skewness-0.38153086
Sum6832808.2
Variance0.3131821
MonotonicityNot monotonic
2024-05-24T18:28:26.531010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
521.9 112
 
0.9%
521.78 110
 
0.8%
521.93 107
 
0.8%
522.02 106
 
0.8%
521.69 105
 
0.8%
521.71 104
 
0.8%
521.85 104
 
0.8%
521.74 103
 
0.8%
522.17 103
 
0.8%
522.13 102
 
0.8%
Other values (347) 12040
91.9%
ValueCountFrequency (%)
519.38 1
< 0.1%
519.39 1
< 0.1%
519.44 1
< 0.1%
519.55 2
< 0.1%
519.58 1
< 0.1%
519.6 1
< 0.1%
519.66 1
< 0.1%
519.67 1
< 0.1%
519.68 1
< 0.1%
519.71 1
< 0.1%
ValueCountFrequency (%)
523.76 1
< 0.1%
523.44 1
< 0.1%
523.42 1
< 0.1%
523.37 1
< 0.1%
523.33 2
< 0.1%
523.29 1
< 0.1%
523.28 1
< 0.1%
523.21 1
< 0.1%
523.2 2
< 0.1%
523.18 2
< 0.1%

(Corrected Fan Speed) (rpm) - s13
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2388.071
Minimum2387.89
Maximum2388.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.6 KiB
2024-05-24T18:28:26.679132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2387.89
5-th percentile2387.98
Q12388.03
median2388.07
Q32388.11
95-th percentile2388.17
Maximum2388.32
Range0.43
Interquartile range (IQR)0.08

Descriptive statistics

Standard deviation0.05693431
Coefficient of variation (CV)2.3841129 × 10-5
Kurtosis-0.028471718
Mean2388.071
Median Absolute Deviation (MAD)0.04
Skewness0.29053324
Sum31274178
Variance0.0032415157
MonotonicityNot monotonic
2024-05-24T18:28:26.842269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
2388.07 909
 
6.9%
2388.06 886
 
6.8%
2388.04 848
 
6.5%
2388.05 845
 
6.5%
2388.03 812
 
6.2%
2388.08 783
 
6.0%
2388.09 773
 
5.9%
2388.11 754
 
5.8%
2388.02 749
 
5.7%
2388.1 735
 
5.6%
Other values (33) 5002
38.2%
ValueCountFrequency (%)
2387.89 3
 
< 0.1%
2387.9 1
 
< 0.1%
2387.91 1
 
< 0.1%
2387.92 8
 
0.1%
2387.93 24
 
0.2%
2387.94 37
 
0.3%
2387.95 69
 
0.5%
2387.96 108
 
0.8%
2387.97 171
1.3%
2387.98 292
2.2%
ValueCountFrequency (%)
2388.32 2
 
< 0.1%
2388.3 1
 
< 0.1%
2388.29 1
 
< 0.1%
2388.28 2
 
< 0.1%
2388.27 7
 
0.1%
2388.26 8
 
0.1%
2388.25 13
 
0.1%
2388.24 16
 
0.1%
2388.23 33
0.3%
2388.22 44
0.3%

(Corrected Core Speed) (rpm) - s14
Real number (ℝ)

HIGH CORRELATION 

Distinct3786
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8138.9478
Minimum8108.5
Maximum8220.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.6 KiB
2024-05-24T18:28:26.997746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum8108.5
5-th percentile8124.56
Q18132.31
median8138.39
Q38144.36
95-th percentile8154.075
Maximum8220.48
Range111.98
Interquartile range (IQR)12.05

Descriptive statistics

Standard deviation10.188605
Coefficient of variation (CV)0.0012518332
Kurtosis6.3030939
Mean8138.9478
Median Absolute Deviation (MAD)6.02
Skewness1.3576338
Sum1.0658766 × 108
Variance103.80767
MonotonicityNot monotonic
2024-05-24T18:28:27.146517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8141.99 15
 
0.1%
8133.28 13
 
0.1%
8140.51 13
 
0.1%
8138.31 13
 
0.1%
8141.03 12
 
0.1%
8138.96 12
 
0.1%
8145.08 12
 
0.1%
8133.1 12
 
0.1%
8137.43 12
 
0.1%
8132.98 12
 
0.1%
Other values (3776) 12970
99.0%
ValueCountFrequency (%)
8108.5 1
< 0.1%
8109.03 1
< 0.1%
8111.16 1
< 0.1%
8111.3 1
< 0.1%
8111.8 1
< 0.1%
8112.19 1
< 0.1%
8112.45 1
< 0.1%
8112.58 1
< 0.1%
8112.64 1
< 0.1%
8113.02 1
< 0.1%
ValueCountFrequency (%)
8220.48 1
< 0.1%
8218.13 1
< 0.1%
8217.24 1
< 0.1%
8214.64 1
< 0.1%
8214.33 1
< 0.1%
8213.57 1
< 0.1%
8213.28 1
< 0.1%
8211.53 1
< 0.1%
8211.21 1
< 0.1%
8210.85 1
< 0.1%

(Bypass Ratio) - s15
Real number (ℝ)

HIGH CORRELATION 

Distinct1506
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.425844
Minimum8.3328
Maximum8.5414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.6 KiB
2024-05-24T18:28:27.279432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum8.3328
5-th percentile8.3801
Q18.4056
median8.4249
Q38.4443
95-th percentile8.475925
Maximum8.5414
Range0.2086
Interquartile range (IQR)0.0387

Descriptive statistics

Standard deviation0.029009328
Coefficient of variation (CV)0.0034428987
Kurtosis0.11255642
Mean8.425844
Median Absolute Deviation (MAD)0.0194
Skewness0.26555184
Sum110344.85
Variance0.00084154108
MonotonicityNot monotonic
2024-05-24T18:28:27.408531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.4223 30
 
0.2%
8.4347 29
 
0.2%
8.4247 28
 
0.2%
8.4282 27
 
0.2%
8.412 26
 
0.2%
8.4344 26
 
0.2%
8.4098 25
 
0.2%
8.4054 25
 
0.2%
8.4313 25
 
0.2%
8.4255 25
 
0.2%
Other values (1496) 12830
98.0%
ValueCountFrequency (%)
8.3328 1
< 0.1%
8.333 1
< 0.1%
8.3332 1
< 0.1%
8.3359 1
< 0.1%
8.3392 1
< 0.1%
8.3414 1
< 0.1%
8.3417 1
< 0.1%
8.3445 1
< 0.1%
8.3447 1
< 0.1%
8.3451 2
< 0.1%
ValueCountFrequency (%)
8.5414 1
< 0.1%
8.5375 1
< 0.1%
8.5374 1
< 0.1%
8.5359 1
< 0.1%
8.5354 1
< 0.1%
8.5343 1
< 0.1%
8.534 1
< 0.1%
8.5294 1
< 0.1%
8.5293 1
< 0.1%
8.5278 1
< 0.1%

(Bleed Enthalpy) - s17
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean392.57162
Minimum389
Maximum397
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.6 KiB
2024-05-24T18:28:27.549861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum389
5-th percentile391
Q1392
median393
Q3393
95-th percentile395
Maximum397
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2335768
Coefficient of variation (CV)0.0031422975
Kurtosis0.10779458
Mean392.57162
Median Absolute Deviation (MAD)1
Skewness0.21669889
Sum5141118
Variance1.5217118
MonotonicityNot monotonic
2024-05-24T18:28:27.662014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
392 3962
30.3%
393 3911
29.9%
391 2003
15.3%
394 1981
15.1%
395 642
 
4.9%
390 412
 
3.1%
396 123
 
0.9%
389 36
 
0.3%
397 26
 
0.2%
ValueCountFrequency (%)
389 36
 
0.3%
390 412
 
3.1%
391 2003
15.3%
392 3962
30.3%
393 3911
29.9%
394 1981
15.1%
395 642
 
4.9%
396 123
 
0.9%
397 26
 
0.2%
ValueCountFrequency (%)
397 26
 
0.2%
396 123
 
0.9%
395 642
 
4.9%
394 1981
15.1%
393 3911
29.9%
392 3962
30.3%
391 2003
15.3%
390 412
 
3.1%
389 36
 
0.3%

(High-Pressure Turbines Cool Air Flow) - s20
Real number (ℝ)

HIGH CORRELATION 

Distinct103
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.892502
Minimum38.31
Maximum39.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.6 KiB
2024-05-24T18:28:27.778621image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum38.31
5-th percentile38.65
Q138.8
median38.9
Q338.99
95-th percentile39.12
Maximum39.41
Range1.1
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.14168076
Coefficient of variation (CV)0.003642881
Kurtosis0.19524364
Mean38.892502
Median Absolute Deviation (MAD)0.09
Skewness-0.23325231
Sum509336.2
Variance0.020073436
MonotonicityNot monotonic
2024-05-24T18:28:28.276003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.9 385
 
2.9%
38.89 379
 
2.9%
38.95 377
 
2.9%
38.88 375
 
2.9%
38.91 374
 
2.9%
38.92 374
 
2.9%
38.97 364
 
2.8%
38.93 360
 
2.7%
38.94 359
 
2.7%
38.96 358
 
2.7%
Other values (93) 9391
71.7%
ValueCountFrequency (%)
38.31 1
 
< 0.1%
38.33 1
 
< 0.1%
38.35 4
< 0.1%
38.36 2
 
< 0.1%
38.37 3
< 0.1%
38.38 1
 
< 0.1%
38.39 2
 
< 0.1%
38.41 1
 
< 0.1%
38.42 5
< 0.1%
38.43 3
< 0.1%
ValueCountFrequency (%)
39.41 1
 
< 0.1%
39.4 1
 
< 0.1%
39.36 3
< 0.1%
39.34 1
 
< 0.1%
39.33 2
 
< 0.1%
39.31 4
< 0.1%
39.3 3
< 0.1%
39.29 5
< 0.1%
39.28 7
0.1%
39.27 5
< 0.1%

(Low-Pressure Turbines Cool Air Flow) - s21
Real number (ℝ)

HIGH CORRELATION 

Distinct3555
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.335743
Minimum22.9354
Maximum23.6419
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.6 KiB
2024-05-24T18:28:28.401981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum22.9354
5-th percentile23.191275
Q123.2816
median23.3392
Q323.3936
95-th percentile23.4681
Maximum23.6419
Range0.7065
Interquartile range (IQR)0.112

Descriptive statistics

Standard deviation0.08412028
Coefficient of variation (CV)0.0036047826
Kurtosis0.15330738
Mean23.335743
Median Absolute Deviation (MAD)0.0558
Skewness-0.24723334
Sum305604.89
Variance0.0070762215
MonotonicityNot monotonic
2024-05-24T18:28:28.523267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.3518 15
 
0.1%
23.3756 15
 
0.1%
23.3727 13
 
0.1%
23.3662 13
 
0.1%
23.3349 13
 
0.1%
23.3075 13
 
0.1%
23.3472 13
 
0.1%
23.3785 12
 
0.1%
23.3931 12
 
0.1%
23.4031 12
 
0.1%
Other values (3545) 12965
99.0%
ValueCountFrequency (%)
22.9354 1
< 0.1%
22.985 1
< 0.1%
23.0071 1
< 0.1%
23.0104 1
< 0.1%
23.0121 1
< 0.1%
23.02 1
< 0.1%
23.0242 1
< 0.1%
23.0345 1
< 0.1%
23.0349 1
< 0.1%
23.0403 1
< 0.1%
ValueCountFrequency (%)
23.6419 1
< 0.1%
23.6229 1
< 0.1%
23.6021 1
< 0.1%
23.6003 1
< 0.1%
23.5863 1
< 0.1%
23.5852 1
< 0.1%
23.5788 1
< 0.1%
23.577 1
< 0.1%
23.5749 1
< 0.1%
23.5707 1
< 0.1%

RUL
Real number (ℝ)

HIGH CORRELATION 

Distinct334
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.23847
Minimum7
Maximum340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.6 KiB
2024-05-24T18:28:28.676255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile43
Q1102
median140
Q3179
95-th percentile243
Maximum340
Range333
Interquartile range (IQR)77

Descriptive statistics

Standard deviation58.980114
Coefficient of variation (CV)0.41759242
Kurtosis-0.032145179
Mean141.23847
Median Absolute Deviation (MAD)39
Skewness0.21821963
Sum1849659
Variance3478.6539
MonotonicityNot monotonic
2024-05-24T18:28:28.831051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140 98
 
0.7%
139 98
 
0.7%
138 98
 
0.7%
137 98
 
0.7%
142 97
 
0.7%
141 97
 
0.7%
145 96
 
0.7%
144 96
 
0.7%
143 96
 
0.7%
146 96
 
0.7%
Other values (324) 12126
92.6%
ValueCountFrequency (%)
7 1
 
< 0.1%
8 4
 
< 0.1%
9 5
< 0.1%
10 7
0.1%
11 8
0.1%
12 8
0.1%
13 8
0.1%
14 9
0.1%
15 10
0.1%
16 11
0.1%
ValueCountFrequency (%)
340 1
< 0.1%
339 1
< 0.1%
338 1
< 0.1%
337 1
< 0.1%
336 1
< 0.1%
335 1
< 0.1%
334 1
< 0.1%
333 1
< 0.1%
332 1
< 0.1%
331 1
< 0.1%

Interactions

2024-05-24T18:28:20.713366image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:41.719191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:44.085576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:45.683769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:47.787568image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:49.523552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:51.421293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:54.473282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:57.439744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:59.742727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:02.967183image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:05.117249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:07.027165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:09.079311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:10.776218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:12.500799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:14.666708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:16.607137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:18.824996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:20.798896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:41.808942image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:44.169967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:45.790817image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:47.866903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:49.600784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:51.540298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:54.622128image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:57.586448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:59.876370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:28:10.857977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:12.581268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:14.782279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:27:51.647794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:54.759048image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:57.713507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:00.041152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:03.264925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:05.308570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:28:09.268025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:28:07.308780image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:28:11.039760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:12.758174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:28:16.966502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:28:21.087608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:27:46.113082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:27:49.855766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:27:52.258598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-05-24T18:28:15.641999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:17.575738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:19.711027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:21.643846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:42.596874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:44.940272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:46.684795image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:48.721637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:50.417134image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:53.155627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:55.923204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:58.702970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:01.729881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:04.064308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:06.159646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:07.967213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:10.002347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:11.698593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:13.578778image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:15.745537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:17.982249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:19.812117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:21.735332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:42.681464image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:45.043312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:46.793684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:48.814358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:50.542641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:53.350532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:56.138162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:58.854016image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:01.904864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:04.172784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:06.256671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:08.077033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:10.087951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:11.782291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:13.719830image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:15.846866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:18.082897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:19.930137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:21.816795image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:42.752619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:45.116769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:46.869242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:48.910582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:50.640256image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:53.525585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:56.315890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:58.973998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:02.023690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:04.270130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:06.346342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:08.160995image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:10.160923image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:11.860415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:13.834103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:15.931343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:18.178994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:20.022735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:21.910055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:42.829325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:45.188225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:46.951197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:48.993008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:50.754362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:53.677067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:56.462784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:59.090346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:02.139304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:04.366070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:06.435657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:08.255810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:10.245916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:11.955558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:13.947336image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:16.022341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:18.266909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:20.117899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:22.043274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:42.911623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:45.265891image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:47.064767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:49.095874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:50.846989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:53.810577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:56.658153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:59.191148image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:02.296836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:04.486490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:06.558564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:08.350711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:10.332292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:12.051088image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:14.078917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:16.115000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:18.356807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:20.215720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:22.150936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:42.989650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:45.368229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:47.172252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:49.177203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:50.942148image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:53.915998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:56.824913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:59.294319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:02.418318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:04.604903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:06.648007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:08.432321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:10.408208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:12.135481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:14.183007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:16.207059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:18.450938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:20.305034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:22.262127image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:43.080044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:45.453972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:47.453648image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:49.275108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:51.076518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:54.057116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:56.980333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:59.421402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:02.535336image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:04.756218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:06.748181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:08.526501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:10.506338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:12.232154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:14.311452image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:16.302103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:18.558342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:20.414245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:22.362772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:43.915172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:45.528428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:47.575723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:49.355743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:51.201223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:54.187020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:57.122322image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:59.529807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:02.661347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:04.876227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:06.840868image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:08.608839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:10.603168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:12.316869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:14.438139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:16.409743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:18.646165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:20.519562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:22.477683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:43.998295image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:45.606267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:47.685529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:49.440245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:51.299208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:54.308181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:57.284685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:27:59.626536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:02.816110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:04.994239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:06.937263image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:08.944954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:10.690854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:12.414241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:14.549613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:16.511677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:18.734758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-24T18:28:20.619933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-05-24T18:28:28.956286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
(Bleed Enthalpy) - s17(Bypass Ratio) - s15(Bypass-Duct Pressure) (psia) - s6(Corrected Core Speed) (rpm) - s14(Corrected Fan Speed) (rpm) - s13(HPC Outlet Pressure) (psia) - s7(HPC Outlet Static Pressure) (psia) - s11(HPC Outlet Temperature) (â—¦R) - s3(High-Pressure Turbines Cool Air Flow) - s20(LPC Outlet Temperature) (â—¦R) - s2(LPT Outlet Temperature) (â—¦R) - s4(Low-Pressure Turbines Cool Air Flow) - s21(Physical Core Speed) (rpm) - s9(Physical Fan Speed) (rpm) - s8(Ratio of Fuel Flow to Ps30) (pps/psia) - s12CycleEngineRULSetting 1 - c1Setting 2 - c2
(Bleed Enthalpy) - s171.0000.4500.125-0.1210.479-0.4970.5160.352-0.4250.3950.490-0.424-0.0130.480-0.5040.3250.081-0.3490.000-0.005
(Bypass Ratio) - s150.4501.0000.140-0.1840.561-0.5690.6060.401-0.4860.4680.564-0.491-0.0610.564-0.5830.3370.084-0.3710.010-0.017
(Bypass-Duct Pressure) (psia) - s60.1250.1401.000-0.0800.163-0.1570.1670.102-0.1320.1220.149-0.142-0.0370.152-0.1650.0750.026-0.084-0.006-0.013
(Corrected Core Speed) (rpm) - s14-0.121-0.184-0.0801.000-0.4540.259-0.251-0.1060.169-0.170-0.2150.1790.827-0.4580.2830.1920.028-0.148-0.0140.024
(Corrected Fan Speed) (rpm) - s130.4790.5610.163-0.4541.000-0.6480.6780.431-0.5430.5110.618-0.545-0.3210.714-0.6760.2660.081-0.3190.016-0.034
(HPC Outlet Pressure) (psia) - s7-0.497-0.569-0.1570.259-0.6481.000-0.674-0.4570.547-0.500-0.6290.5420.123-0.6460.663-0.353-0.0950.399-0.0050.028
(HPC Outlet Static Pressure) (psia) - s110.5160.6060.167-0.2510.678-0.6741.0000.478-0.5840.5450.662-0.576-0.1060.675-0.7030.3890.102-0.4350.009-0.030
(HPC Outlet Temperature) (â—¦R) - s30.3520.4010.102-0.1060.431-0.4570.4781.000-0.3900.3660.439-0.390-0.0070.436-0.4640.2840.062-0.3200.005-0.012
(High-Pressure Turbines Cool Air Flow) - s20-0.425-0.486-0.1320.169-0.5430.547-0.584-0.3901.000-0.453-0.5530.4690.055-0.5490.569-0.341-0.0910.370-0.0000.009
(LPC Outlet Temperature) (â—¦R) - s20.3950.4680.122-0.1700.511-0.5000.5450.366-0.4531.0000.513-0.443-0.0640.512-0.5350.3060.072-0.3470.010-0.019
(LPT Outlet Temperature) (â—¦R) - s40.4900.5640.149-0.2150.618-0.6290.6620.439-0.5530.5131.000-0.536-0.0840.620-0.6500.3750.096-0.4180.002-0.022
(Low-Pressure Turbines Cool Air Flow) - s21-0.424-0.491-0.1420.179-0.5450.542-0.576-0.3900.469-0.443-0.5361.0000.058-0.5490.567-0.329-0.0770.3670.0090.027
(Physical Core Speed) (rpm) - s9-0.013-0.061-0.0370.827-0.3210.123-0.106-0.0070.055-0.064-0.0840.0581.000-0.3170.1410.2680.047-0.229-0.0090.015
(Physical Fan Speed) (rpm) - s80.4800.5640.152-0.4580.714-0.6460.6750.436-0.5490.5120.620-0.549-0.3171.000-0.6770.2670.079-0.3160.006-0.025
(Ratio of Fuel Flow to Ps30) (pps/psia) - s12-0.504-0.583-0.1650.283-0.6760.663-0.703-0.4640.569-0.535-0.6500.5670.141-0.6771.000-0.363-0.0940.413-0.0120.024
Cycle0.3250.3370.0750.1920.266-0.3530.3890.284-0.3410.3060.375-0.3290.2680.267-0.3631.0000.026-0.671-0.005-0.018
Engine0.0810.0840.0260.0280.081-0.0950.1020.062-0.0910.0720.096-0.0770.0470.079-0.0940.0261.000-0.019-0.0170.003
RUL-0.349-0.371-0.084-0.148-0.3190.399-0.435-0.3200.370-0.347-0.4180.367-0.229-0.3160.413-0.671-0.0191.000-0.006-0.002
Setting 1 - c10.0000.010-0.006-0.0140.016-0.0050.0090.005-0.0000.0100.0020.009-0.0090.006-0.012-0.005-0.017-0.0061.000-0.007
Setting 2 - c2-0.005-0.017-0.0130.024-0.0340.028-0.030-0.0120.009-0.019-0.0220.0270.015-0.0250.024-0.0180.003-0.002-0.0071.000

Missing values

2024-05-24T18:28:22.688855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-24T18:28:23.015209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

EngineCycleSetting 1 - c1Setting 2 - c2(LPC Outlet Temperature) (â—¦R) - s2(HPC Outlet Temperature) (â—¦R) - s3(LPT Outlet Temperature) (â—¦R) - s4(Bypass-Duct Pressure) (psia) - s6(HPC Outlet Pressure) (psia) - s7(Physical Fan Speed) (rpm) - s8(Physical Core Speed) (rpm) - s9(HPC Outlet Static Pressure) (psia) - s11(Ratio of Fuel Flow to Ps30) (pps/psia) - s12(Corrected Fan Speed) (rpm) - s13(Corrected Core Speed) (rpm) - s14(Bypass Ratio) - s15(Bleed Enthalpy) - s17(High-Pressure Turbines Cool Air Flow) - s20(Low-Pressure Turbines Cool Air Flow) - s21RUL
0110.00230.0003643.021585.291398.2121.61553.902388.049050.1747.20521.722388.038125.558.405239238.8623.3735142
112-0.0027-0.0003641.711588.451395.4221.61554.852388.019054.4247.50522.162388.068139.628.380339339.0223.3916141
2130.00030.0001642.461586.941401.3421.61554.112388.059056.9647.50521.972388.038130.108.444139339.0823.4166140
3140.00420.0000642.441584.121406.4221.61554.072388.039045.2947.28521.382388.058132.908.391739139.0023.3737139
4150.00140.0000642.511587.191401.9221.61554.162388.019044.5547.31522.152388.038129.548.403139038.9923.4130138
5160.00120.0003642.111579.121395.1321.61554.222388.009050.9647.26521.922388.088127.468.423839238.9123.3467137
617-0.00000.0002642.111583.341404.8421.61553.892388.059051.3947.31522.012388.068134.978.391439138.8523.3952136
7180.0006-0.0000642.541580.891400.8921.61553.592388.059052.8647.21522.092388.068125.938.421339339.0523.3224135
819-0.00360.0000641.881593.291412.2821.61554.492388.069048.5547.37522.032388.058134.158.435339139.1023.4521134
9110-0.0025-0.0001642.071585.251398.6421.61554.282388.049051.9547.14522.002388.068134.088.409339138.8723.3820133
EngineCycleSetting 1 - c1Setting 2 - c2(LPC Outlet Temperature) (â—¦R) - s2(HPC Outlet Temperature) (â—¦R) - s3(LPT Outlet Temperature) (â—¦R) - s4(Bypass-Duct Pressure) (psia) - s6(HPC Outlet Pressure) (psia) - s7(Physical Fan Speed) (rpm) - s8(Physical Core Speed) (rpm) - s9(HPC Outlet Static Pressure) (psia) - s11(Ratio of Fuel Flow to Ps30) (pps/psia) - s12(Corrected Fan Speed) (rpm) - s13(Corrected Core Speed) (rpm) - s14(Bypass Ratio) - s15(Bleed Enthalpy) - s17(High-Pressure Turbines Cool Air Flow) - s20(Low-Pressure Turbines Cool Air Flow) - s21RUL
13086100189-0.00030.0002643.291592.331417.6621.61553.592388.069136.5547.55520.932388.098209.848.442339338.7123.318829
13087100190-0.00380.0002642.951598.971421.2821.61553.592388.109137.3547.49521.632388.078207.958.476539538.7423.355128
13088100191-0.0031-0.0001642.921589.541413.6521.61553.242388.029136.1947.61521.232388.078201.948.487739638.8923.227927
13089100192-0.00340.0001643.051598.181418.5821.61553.162388.059141.9247.57520.992388.078210.248.417139538.7723.214826
130901001930.00180.0004643.101595.601414.6221.61553.182388.089139.8847.58521.372388.058213.578.442939538.6323.295225
130911001940.00490.0000643.241599.451415.7921.61553.412388.029142.3747.69520.692388.008213.288.471539438.6523.197424
13092100195-0.0011-0.0001643.221595.691422.0521.61553.222388.059140.6847.60521.052388.098210.858.451239538.5723.277123
13093100196-0.0006-0.0003643.441593.151406.8221.61553.042388.119146.8147.57521.182388.048217.248.456939538.6223.205122
13094100197-0.00380.0001643.261594.991419.3621.61553.372388.079148.8547.61521.332388.088220.488.471139538.6623.269921
130951001980.00130.0003642.951601.621424.9921.61552.482388.069155.0347.80521.072388.058214.648.490339638.7023.185520